PLoS computational biology · 2021
A systems genomics approach uncovers molecular associates of RSV severity.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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McCall Matthew N, Chu Chin-Yi, Wang Lu, Benoodt Lauren, Thakar Juilee, Corbett Anthony, Holden-Wiltse Jeanne, Slaunwhite Christopher, Grier Alex, Gill Steven R, Falsey Ann R, Topham David J, Caserta Mary T, Walsh Edward E, Qiu Xing, Mariani Thomas J
Аннотация
Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.